In general, users can create user-generated contents and publish the created contents to other users on an online network such as an online community, a blog, a social network or the like on the internet.
Further, a technique for ranking various posts based on predetermined criteria and searching a desired post has been developed along with the development of the posts and the contents technique. A document ranking technique is used to search a desired post based on the ranking. A conventional document ranking technique is classified into a keyword-based document ranking technique and a document ranking technique using link analysis between web documents.
The keyword-based document ranking technique includes a TF-IDF (Term Frequency-Inverse Document Frequency) technique developed based on a conventional information retrieval theory. This technique utilizes a term frequency in a document and an inverse document frequency of a document frequency including terms.
Meanwhile, the document ranking technique using link analysis between web documents includes a page rank technique for computing rank scores of web documents by analyzing a reciprocal link relationship between the web documents and a HITS (Hyperlink-Induced Topic Search) technique for calculating an authority score and a hub score of each document by analyzing a reciprocal link relationship based on a web document search result.
Meanwhile, users can leave feedbacks on published posts. For example, the users' feedbacks on the posts include read or view, comment, reply, favorite, evaluation and the like.
Here, it should be noted that a post with feedbacks from users shows the users' tendency. For example, high-ranked users of high expertise tend to prefer high expertise posts and give feedbacks on such posts, whereas middle- or low-ranked users of low expertise tend to prefer commercial posts compared to high expertise posts and give feedbacks on such posts. When users read movie posts or image posts, movies or images that are artistic and professional receive high scores from experts of the related area, whereas movies or images that are fun are preferred by the public and receive feedbacks therefrom. In other words, a post preference tendency is different in accordance with user ranks.
FIGS. 1A to 1C show feedback graphs on three posts preferred by different user groups.
Referring to FIGS. 1A to 1C showing the three posts preferred by different user groups, the x-axis indicates user groups, and the y-axis indicates preference of the user groups. A high value in the x-axis indicates a low user group, and a high value in the y-axis indicates high preference.
The post graph of FIG. 1A shows that the corresponding post is relatively preferred by the low-ranked user group; the post graph of FIG. 1B shows the corresponding post is preferred by the middle-ranked user group; and the post graph of FIG. 1C shows that the corresponding post is preferred by the high-ranked user group.
As described above, in the conventional user contents classification method, the user group basis preference posts can be classified based on the users' expertise and reputation measured by the feedbacks on the posts. However, it is difficult to accurately measure similarity between the posts based on the feedbacks.